The use of exemplar information in classification-based and inference-based category learning

A prominent aspect of categorization is our ability to extract categories out of a wide range of items. Unlike most experimental situations where a limited set of category instances is presented repeatedly, we are able to learn about a variety of categories in the world even though each exemplar we see may be unique. Two experiments were designed to capture this aspect of categorization. We presented subjects with one of three types of category learning — Classification learning, Inference Learning or Mixed Learning — and examined how these procedures interacted with different types of stimulus depiction in which each feature of a stimulus was depicted by either a single instance or by a large number of distinct instances.